1. Analysing qualitative data

2. What is the input? - non-numeric data - not quantified - can be a product of all research strategies -> Procedures for analysis can be BOTH deductive and inductive - computer aided qualitative data analysis software (CAQDAS)

3. Computer aided qualitative data analysis software (CAQDAS) …used in psychology, marketing research, ethnography etc +- efficient means to manage and organize data - rigorous data analysis - no manual and clerical tasks - saves time - manages huge amounts of qualitative data - increases flexibility - improves validity and auditability of qualitative research -- increasingly deterministic/ rigid processes - privileging of coding - reification of data - increased pressure to focus on volume/breadth rather than on depth/meaning - time/ energy spent learning to use computer packages - increased commercialism - distraction from the real work of analysis

4. Differences between qualitative and quantitative data Quantitative data: - Based on meanings derived from numbers - Collection result in numerical and standardised data - Analysis conducted through the use of diagrams and statistics Qualitative data: - Based on meanings expressed through words - Collection results in non- standardised data requiring classification into categories -Analysis conducted through the use of conceptualisation

5. Preparing your data for analysis: transcribing qualitative data non-verbal information may be relevant (pauses, laugh, sighs, coughs, the tone of the voice, the speed of talk) – not only, what they say but how they say it awfully time-consuming – 6-10 h to transcribe every hour of audio- recording Accurateness of transcription – data cleaning Save each interview as separate file; use filename that maintains confidentiality/ anonymity; helps recognize the the person Distinguish between interviewer and participant(s) visually; use other identifiers – questions in italics, topic headings in bold etc; be consistent across all transcriptions Having the full question in transcript may be of importance if you want to understand later what they are talking about :P Plan in advance, how the analysis will follow – e.g., if you use some CAQDAS, remember, that they may require sometimes.txt file so all your highlights, capitals and italics will be gone :P

6. Aga nüüd vaatasime seda lõiku ja sa nägid seda enne ka ja sa ütlesid, et ta võttis selle telefoni ära. Mis sa arvad, miks ta selle ära võttis? V:Ta tahtis endale saada. K:Vist küll. A siin oli üks teine tegelane veel. See mees. Kas tema ka midagi valesti tegi sinu arvates? V:Jah, et ta ei hoiatand teda. K:Aga mis sa arvad, miks ta ei hoiatanud? V:Ei tea K:Mis siin valesti tehti? V:[ei saa aru] et siin nad võtsid selle koti ära ja viskasid ära, et ta ei saaks seda kätte. Too teine, kes seda pealt nägi, nemad ei hoiatand seda poissi. K:Täpselt. Mis sa arvad, miks need kaks poissi seda väiksemat siis niimoodi kiusasid? V:Et neile vist meeldis. K:Aga miks see tädi, kes seal juures oli, miks ta appi ei läinud? Mis sa arvad? V:Ta tegeles parajasti millegi muuga ja tal polnd tahtmist appi minna. K:Jah, ma arvan, et sul on õigus. Mis siin siis valesti tehti? V:Et [ei sa aru] aga ta tegelt oskas seda ise ka teha. K:A sa arvad, et oskas ise ka. A mis sa arvad, miks see tädi ei aidanud? V: Ta ei tahtnud vist. K:Vist jah. Nonii, mis siin valesti tehti? V:Et nagu üks nagu midagi ütles, mingi suvaline inimene, lihtsalt, mis kell on. Et ta küsis lihtsalt, mis kell on. Et nagu vabandada, seda ta ei ütlendki K:Ahah. Mis sa arvad, miks ta ei tahtnud öelda? V: Sellepärast et ta seal mõtles, et mingi suvaline inimene ja pole pole üldse lahke, et ta ei tahtnud talle ütelda.

7. An overview of qualitative anaysis: four main categories of strategies Understanding the characteristics of language Discovering regulatities Comprehending the meaning of text or action Reflection Dimensions to differentiate the approaches to qualitative analysis: Less structured  -------  More structured Interpretivist  ------  Procedural Inductive  -------  Deductive

8. Basic procedures common to different approaches of qualitative data analysis: 1) categorisation Helps you: - Comprehend and manage your data; - Integrate related data drawn from different transcripts and notes; - Identify key themes or patterns from data for further exploration; - Develop and/or test theories based on these apparent patterns and relatioships; - Draw and verify conclusions

9. Categories: -may be derived from these data or from your theoretical framework -Have to „fit“ with what you have revealed – with data -Codes/ labels, giving a structure for the data -Identification of the categories -> purpose of your research -it is possible to interprete the same qualitative data very differently -Internal aspect of category – meaningful in relation to the data -External aspect of category – meaningful in relation to other categories

10. 2) „Unitising“ data - unit - chunk or bit of textual data that fits the category and carries discrete meaning 3) Recognising relationships and developing categories -search for key themes /patterns /relationships -revise your categories -keep an up-to-date definition of all the categories

11. 4) Developing and testing hypotheses or propositions -testing relationships between variables -seeking alternative explanations/ negative examples -considering possible intervening variables

12. Analytical aids: a record of additional contextual information Summaries – after every data collection set -> a summary of the key points that have arised; think on alternative ideas to explore your question; identify apparent relationships between themes -> check their validity; contextual notes – setting, changes, persons etc. Self-memos – to record ideas about any aspect of your research. Omitting to record an idea -> it will be lost – it is proved! Researcher’s diary - recording ideas -> you can later follow the development of them because of the choronogical form

13. Approaches to qualitative analysis Deductive – using a theoretical or descriptive framework - use of existing theory to formulate research question -> theoretical propositions may devise a framework to organise/ direct the data analysis. -Advantage – link your research into the existing body of knowledge in your subject area Inductive – exploring without a predetermined theoretical or descriptive framework - to start collecting data/ exploring them -> finding themes to concentrate on. -Analyse the data during collecting it, developing a conceptual framework to guide the subsequent work In practice -> combining the elements from both approaches as at certain points you may need to develop some theoretical position to test its applicability; and at some moment you notice that the theoretical framework you choosed does not yield a good answer to your research question

14. Deductively-based analytical procedures Pattern matching - predicting a pattern of outcomes based on theoretical propositions to explain what you expect to find. Two variations Explanation building – an attempt to build an explanation while collecting data and analysing them. process of explanation building - iterative

15. Inductively-based analytical procedures Reasons for adopting an inductive approach for the analysis of data: 1)need for an exploratory project seeking to generate a direction for further work 2)the scope of your research -> constrained by theoretical propositions not reflecting participant’s views / experience. The use of inductive approach should allow a good „fit“ between the theory you develop and the social reality of the participants 3)the theory may be used to suggest subsequent action to be taken because it is specifically derived from the events and circumstances of the setting in which the research was conducted You should NOT USE inductive approach to avoid a proper level of preparation!

16. Data display and analysis 1)summarise and simplify the data; selectively focus on some parts of it; the aim is to transform and condense the data 2)organise and assemble your reduced and selected data into some diagrammatic or visual displays (matrix or network); recognizing the relationships and patterns/ drawing conclusions and verifying these is easier by the use of data displays

17. Template analysis -list of the codes or categories that represent themes revealed from the data. -The codes will be predetermined and then amended/ added if the data requires it -Data are coded and analysed to identify and explore themes, patterns and relationships. -codes and categories can be shown hierarchically -The codes at different level of analysis may change their position during the process -What’s the point of all this? -…analytical technique through which to devise an initial conceptual framework that will represent and explore key themes and relationships in the data; help you to identify new, emergent issues that arise through the process of data collection and analysis

18. Analytic induction …inductive version of the explanation-building procedure – „intensive examination of a strategically selected number of cases so as to empirically establish the causes of a specific phenomenon“.

19. Grounded theory …to build an explanation or to generate a theory around the central theme that emerges from your data. The process may be more or less structured and systematic. There are different stages of grounded theory procedures: Open coding – the data will be disaggregated into conceptual units and provided with a label. In that way you may find a multitude of labels, that you need to place into broader, related groupings or categories. This will produce a more manageable and focused data set Axial coding – process of looking for relationships between the categories of data that have emerged from open coding. As relationships between categories are recognised, they are re-arranged into a hierarchical form, with the emergence of subcategories. The aim is to explore and explain the phenomenon by identifying what is happening and why; to find out what environmental factors affect this; how it is being managed within the context being examined, and what the outcomes are of the action that has been taken. Selective coding – during data collection, it is likely that you will find the principal categories and related subcategories – core categories will be base of your grounded theory

20. Quantifying qualitative data -to count the frequency of certain events, particular reasons that have been given, or in relation to specific references to a phenomenon -frequencies can be displayed as a table or diagram -can be produced using CAQDAS programs; exported to statistical analysis software -considered as method of limited value -> do not demonstrate the nature and value of your qualitative data, being a simplified form of it